Kadir Has Meslek Yüksekokulu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/83

Browse

Browsing Kadir Has Meslek Yüksekokulu by Author "Kabaoğlu, Nihat"

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Citation Count: 0
    Near field parameter estimation of moving sources with recursive expectation maximization algorithm
    (IEEE, 2006) Cekli, Serap; Cekli, Erdinc; Kabaoğlu, Nihat; Cirpan, Hakan Ali
    In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented.
  • Thumbnail Image
    Conference Object
    Citation Count: 0
    Near field parameter estimation of moving sources with recursive expectation maximization algorithm [Yinelemeli beklenti/en büyükleme algoritması ile hareketli kaynakların yakın-alan parametrelerinin kestirimi]
    (2006) Çekli, Serap; Çekli, Erdinç; Kabaoğlu, Nihat; Cirpan, Hakan Ali
    In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented. © 2006 IEEE.
  • Thumbnail Image
    Conference Object
    Citation Count: 9
    Unconditional maximum likelihood approach for localization of near-field sources in 3-D space
    (IEEE, 2004) Kabaoğlu, Nihat; Çırpan, Hakan Ali; Paker, Selçuk
    Since maximum likelihood (ML) approaches have better resolution performance than the conventional localization methods in the presence of less number and highly correlated source signal samples and low signal to noise ratios we propose unconditional ML (UML) method for estimating azimuth elevation and range parameters of near-field sources in 3-D space in this paper Besides these superiorities stability asymptotic unbiasedness asymptotic minimum variance properties are motivated the application of ML approach. Despite these advantages ML estimator has computational complexity. Fortunately this problem can be tackled by the application of Expectation/Maximization (EM) iterative algorithm which converts the multidimensional search problem to one dimensional parallel search problems in order to prevent computational complexity.